Power-optimized cumulative, sequential statistical method for detection of auditory evoked potentials

ABSTRACT

A method for determining the statistical probability that an auditory brainstem response (ABR) to an acoustic stimulus is present in a human test subject. The method employs an algorithm that provides a continuously evolving estimate of the probability of ABR presence as acquired data accumulates. The algorithm utilizes a Hotelling T 2  test.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to the field of assessinghearing capacity in humans. More particularly, the invention is a methodfor quantifying the probability that an auditory brainstem response(ABR) is present in an electrophysiologic recording from a human infant.

2. Background of the Invention

The ABR is a waveform of fluctuating electrical potential over time,which may occur in response to a brief, transient acoustic stimulus suchas a click. The ABR originates in the neurons of the auditory nerve andits higher connections in the brain stem. When recorded from electrodeson the scalp or neck, it is less than one microvolt in size, and isobscured by much larger ongoing random potentials that arise elsewherein the brain and the musculature of the head and neck. Computersummation or averaging of the responses to several thousand stimulipresented at rates typically in the range of 20-50 per second isrequired to enhance the ABR “signal” relative to the backgroundelectrical “noise”, and to render it visually detectable in the summedor averaged response.

The presence or absence of an ABR for a specific type and intensity ofstimulus can be used as a proxy for overt behavioral response,indicating whether or not the stimulus was audible. This is the basisfor an electrophysiologic hearing screening test, of particular value insubjects such as infants who are unable to give reliable, behavioralresponses to sound.

In the newborn population, it is widely acknowledged that it isimportant to detect and manage hearing loss as early as possible, andpreferably in the first six months, to facilitate development of speech,language and cognitive skills. In 1993, the National Institute onDeafness and Other Communication Disorders sponsored a ConsensusConference on Early Identification of Hearing Impairment in Infants andYoung Children. That conference recommended screening for identificationof hearing impairment in the newborn period for all infants regardlessof the presence or absence of risk factors for hearing loss, that is,universal infant hearing screening. These recommendations were endorsedand reiterated soon after by the American Academy of Audiology and theJoint Committee on Infant Hearing, 1984. Many states have recentlyimplemented, or are in the process of implementing, such screeningprograms. This widespread endorsement of mass hearing screening ofneonates and infants has created a challenge for scientists andclinicians to have fast and accurate tools ready for evaluatingpotential hearing loss in infants.

ABR testing is well established as a core part of most screeningprotocols. The clinical utility of ABR-based hearing screening testsdepends critically on the accuracy of the ABR detection decisions. Suchdecisions are intrinsically prone to error, because they involve thedetection of a signal in random noise that may obscure a genuine signalor masquerade as a signal when none is present. False-positive ABRdetection leads to a false-negative screening test: the hearing-impairedchild passes the screening test and receives no intervention. Othermanifestations of disorder may be ignored, given that the test waspassed, so the screening does active harm. False-negative ABR detectiontests cause false-positive screening tests; this precipitates needlessfollow-up diagnostic assessment costs, as well as indirect costs ofmislabeling a normal child.

A distinction must be drawn between detection tests that are empiricaland those that are analytic. Empirical tests are based upon experimentalstudies of the distributions of a given test statistic when response isthought to be present or absent. Usually the determination of responsepresence or absence is based on expert subjective assessment of theaverage records obtained in a set of subjects. There are two majordifficulties with this approach. First, the expert judgments may bewrong, which clearly confounds the assessment of the accuracy of thetest statistic. Second, there is no proof that the results observed inone set of subjects will necessarily apply to a different set ofsubjects or to a situation in which any feature of the data recording oranalysis is changed. This is a failure of generalizability of theempirical validation process.

Analytic methods, in contrast, do not appeal to experimental validationdatasets. They are based upon known properties of known statisticaldistributions relating to the chosen test statistic. Thus, rather thanrelying on empirical experimental data, analytic methods capitalize uponthe vast body of statistical distribution theory and statistical tablesof distributions. It is necessary to show that real data satisfy certainassumptions that are required for certain distributions to pertain, butthese assumptions may be weak, easily satisfied, and easily proven tohold. Such methods are both highly quantitative, yielding known andspecifiable rates of decision error, and are also highly generalizibleacross datasets and measurement conditions.

A crucial characteristic of a good statistical response detection testis that it has the highest possible statistical power. Power is theprobability that the test will correctly detect a response that isgenuinely present. Less than optimal test power is very disadvantageousin practical terms. A loss of power translates directly to longer testtime than necessary to reach the statistical criterion for responsedetection. This is a major practical disadvantage because some babiesyield satisfactory measurement conditions for only brief periods oftime, they may be untestable due to test inefficiency. Also testablebabies will take longer to test than necessary which increases costs anddecreases throughput. This factor will be especially crucial in light ofthe implementation of universal newborn hearing evaluation protocols nowmandated in many states. Third, the use of a test that is less powerfulthan necessary will result in larger rates of detection decision errorthan would be possible with a more powerful test.

Prior Art Detection Systems

Current approaches to automated detection of ABRs include techniquesthat evaluate the time-domain waveform and those that assess spectralcharacteristics (frequency domain). Automated detection of neonatalclick-evoked ABR to low-level stimuli for mass screenings have primarilyinvolved analysis in the time domain, although one known system includesboth time and frequency domain analysis. At present, four systems havebeen used or sold as “automated infant ABR screening” devices. By thatwe refer to those devices in which decisions regarding ABR presence orabsence or test “Pass” or “Fail” (sometimes called “Refer”) is made bythe system itself (not by the examiner) based on some predeterminedcriteria that are discussed below.

The general approach of the detection algorithm employed by the mostcommonly used system for automated ABR detection in infants appears tobe as follows: A set of sample points are weighted according to theirrelative magnitude in the standard infant ABR waveform. It is not clearhow the position or number of the data points are selected. Thepolarities of the amplitude of each point in a standard or template arecompared with those observed at the corresponding latency in each sweepduring averaging. Each time a sweep is sampled, the correspondence ofpolarity between the data and the template at each of the selected timepoints yields a count of +1. After every 500 sweeps, the template pointsare shifted in increments of 0.25 ms over a 3 ms range to locate theposition of maximum polarity correspondence. Presumably, this is doneusing an accumulated average of some kind, but this is not clear. Eachsample in each sweep constitutes a trial and running counts of thenumbers of polarity matches and trials are accumulated. Because theprobability of a polarity match for each point is 0.5 if the response isabsent, a quantitative hypothesis test can be constructed based on abinomial model. This technique appears to be a combination of templatecross-correlation with a multi-point amplitude-based detection criterionafter a one-bit conversion.

The detection algorithm used in this system is statistically based butis far from analytic. Specific disadvantages are as follows:

Lack of validity: The algorithm effectively counts the number of timesthe polarity of the recorded activity matches the expected polarity ofan ABR template waveform. Several points are tested per sweep and thenumber of polarity coincidences is also summed over many sweeps.

Because the successive data points within each sweep are notstatistically independent, the sampling distribution of the number ofcoincidences will not have the binomial distribution that is assumed.This means that the actual error rates of the test are not representedaccurately in statistical tables. Therefore, the method is substantiallyempirical. Actual error rates may only be determined by experiment withquantitivity and generalizability limitations noted earlier.

Power Sub-optimality: This detection algorithm counts correspondenceevents between observed and expected polarity of activity at specifictimes. The actual amplitude of the observed signal is not fullyutilized, only the polarity. An analogy can be drawn to the use of theOrdinary Sign Test instead of the Student t-test to examine thehypothesis that the true mean of a sample of n observations is zero. Thesign test uses only the polarity of the data, whereas the t-test, whichis the most powerful test possible under the assumption of normal errordistributions, uses all of the amplitude information. The asymptoticrelative efficiency of the sign test is 2/pi, or 64%. This implies thatany sign-based detection method will sustain a substantial loss ofpower.

Another commercially available instrument for ABR hearing screeningincludes automatic detection that is based on the following algorithm:For any particular stimulus level, the system acquires two ABRs with afixed stimulus level. The averages are stopped if the estimatedsignal-to-noise ratio exceeds one or after 1,024 sweeps. If bothaverages have SNR>1, the response is deemed present. If not, a crosscorrelation analysis is performed. The latency region of 5 to 12.5 mspost-stimulus is sectioned into seven overlapping ‘windows’, each of 2ms duration. For each window position, a Pearson correlation coefficientof the data values in the two averages for each and every successivetime point in the given window is calculated. The test variable is themaximum absolute value among the seven correlations covering all windowpositions. If the test variable exceeds 0.9, the ABR is deemed to bepresent. This approach to automatic detection is an adaptation of asimple, correlation-based detection method first reported for the ABR byWeber, B. A and Fletcher, G. L., 1980 A Computerized Scoring Procedurefor Auditory Brainstem Response Audiometry. Ear and Hearing, 1, 233-236.(1980).

The detection algorithm of this device is highly empirical. The primarydetection statistic is a cross correlation coefficient between twoindependent averages using the region of anticipated response. The teststatistic is the absolute maximum of the observed correlations. Becauseof the extensive correlation (autocorrelation) between successive datavalues in each of the averages, the statistical distribution of the teststatistic is unknown, and detection error rates cannot be derived fromstatistical tables. The critical values for the test statistic, and theerror rates, can only be estimated by experiment. Indeed, they wereselected using empirical data with expert subjective judgment as thegold standard for response presence or absence. The serious limitationsof this method were described earlier.

Details of response detection in a third prior art system areproprietary, although the manufacturer has released a non-detaileddescription of the decision-making system. Briefly, the system evaluatesthree aspects of the infant ABR in the decision process. First, thesystem determines presence or absence of an ABR in a record byevaluating (a) the presence of a predetermined spectral component of theresponse, using a multivariate analysis simultaneously assessing bothreal and imaginary components of a specified Fourier component and (b)an F_(SP)-like signal to noise estimate. If those criteria are met, thewaveform morphology is checked with a type of template match thatevaluates certain features of the waveform (peak number and placement).It appears that all three aspects of response detection algorithm mustbe satisfied for an infant to receive a “pass” from this system.

Limited information is available about this proprietary detectionalgorithm. It is based on a combinatorial approach using four types ofmeasure: template (wave shape) and non-template features in both thetime and frequency domains. The frequency domain algorithm involvesexamining the distribution of sine and cosine parts of several harmonicsof the Fourier spectrum of the recorded activity, and a comparison withthe expected values for both noise and ABR signals. This is combined inan unknown manner with a “modification of the so-called F_(SP)technique”. The detection stage is followed by a verification stage thatexamines the extent to which the detected and estimated waveform matchesexpected waveshape characteristics.

The performance of this approach is not known and not derivable fromstatistical distribution theory. The multi-component nature of themethod virtually guarantees that it is not of analytic strength, butthat it will be empirical. An alleged advantage is its exploitation ofboth time-domain and frequency-domain features. This is highlyquestionable, because the time-history and Fourier spectrum of anyactivity are linear transformations of each other and contain identicalunderlying information.

F_(SP)

A fourth prior art technique that has been applied to infant ABRdetection for screening is the F_(SP) (This technique is described inElberling, C. & Don, M. (1984). Quality Estimation of Averaged AuditoryBrainstem Responses. Scand Audiol, 13, 187-197 and Don, M., Elberling,C. & Waring, M. (1984). Objective Detection of Averaged AuditoryBrainstem Responses. Scand.Audiol 13, 219-228.). This technique is notapplied commercially for specific use in infant screening but isavailable on some commercial evoked potential systems for general use(Neuroscan and Nicolet “Spirit”) and was applied to automated newbornhearing screening by the first named inventor of the present inventionin a multi-center study funded by the National Institute on Deafness andOther Communication Disorders. F_(SP) involves calculation of a varianceratio (hence the F) the numerator of which is essentially the samplevariance of the average and the denominator of which is the variance ofthe set of data values at a fixed single point (hence the “SP”) in thetime window across a group of sweeps.

F_(SP) is used to estimate the “quality” or the signal-to-noise ratio ofan auditory evoked potential. Calculation of F_(SP) is based on the factthat any ABR recording is background noise (random brain and muscleactivity not related to the auditory signal) and, if the signal isaudible to the subject, each recording also contains neural activityfrom the auditory system that is systematic in scalp recorded morphologyand time-locked to the onset of the eliciting auditory signal. For anygiven single, digitized time point in the averaged ABR waveform, theneural contribution to the amplitude measured at that point is constantfrom sweep to sweep whereas the noise contribution to amplitude shouldbe random. Consequently, the neural response will contribute nothing tothe variance of the amplitude at any single point and the sweep to sweepvariance of a single point in the analysis window can be used as anaccurate estimator of the variance of the background noise in therecording. This is referred to as VAR(sp).

Calculation of F_(SP) is illustrated in FIG. 1. The magnitude of theaveraged response can be characterized by the point to point variance ofthe digitized amplitude measures for a specified window of the average.In the standard F_(SP) calculation, each point across a specified timewindow is used in a standard variance calculation referred to as VAR(s).This value is comprised of the energy of the ABR (if present) as well asthe energy of the averaged noise. Every 256 sweeps the averaging processis halted momentarily and VAR(s) and VAR(sp) and the ratio of the two(F_(SP)) is calculated. The numerator or VAR(s) includes signal andnoise and the denominator or VAR(sp) estimates noise. When no signal(ABR) is present the expected value of the ratio is close to 1. Theratio of variances has the known statistical F distribution, indexed bya parameter known as the degrees of freedom (dof). Consequently, whenthe degrees of freedom are known, the probability of false positivedetection for any F_(SP) value associated with an evoked potentialrecording can be determined by look up on an F table.

In a standard paradigm, F_(SP) values are updated after each 256 sweeps.As the averaging process reduces background noise, the F_(SP) valueassociated with a recording containing a true ABR, will grow. A priorirules can be established for halting of the averaging process based on acomparison of achieved and desired probability of true responsedetection. For example, in the article cited above, Elberling and Don,(1984) used a conservative estimation of degrees of freedom anddetermined that F_(SP) of 3.1 would correspond to true-positivedetection confidence of 99%. In that case, the F_(SP) value was used asthe stopping criterion for the averaging process, indicating that thedesired signal to noise ratio had been achieved. Because any givenrecording or subject will vary dramatically in the level of thebackground noise and the amplitude of the evoked potential, using atarget F_(SP) as a stopping rule optimizes the use of averaging time,averaging shorter periods of time in good SNR and longer in poor SNRconditions.

The disadvantages of the F_(SP) technique include:

Excessive window length: The standard response analysis window haslength 1000/HPF ms where HPF is the high-pass cutoff frequency of therecording amplifier. For a typical case of HPF of 100 Hz, the length is10 ms. This is generally greater than the length of the region ofsignificant response amplitude. Thus, time regions that contributelittle or nothing to the numerator variance estimate are included. Thisreduces the expected value of the numerator, resulting in a lesssensitive test (a test with lower statistical power) than if the windowwere delimited to regions of substantial response amplitude.

Sub-optimal test points: Even given a response-focused window, some timepoints within the window contribute more to the response variance thando others. In general, there will exist some subset of all the points inthe window that develops maximum variance for a given response waveform,and there will be many other subsets that develop variance substantiallygreater than the variance of the entire window. It follows that even fora focused window, to select all points in the window as is done in thestandard F_(SP) is sub-optimal with respect to statistical power. Bothof these disadvantages result in a detection test that is less powerfulthan necessary.

Conventional F_(SP) can be classed as semi-empirical or semi-analytic.The approach is vastly more quantitative and reproducible than issubjective judgment of response presence or absence. The limitationarises from the fact that the statistical degrees of freedom in thenumerator variance estimate are known only approximately. This is due tothe fact that the effective degrees of freedom in a time series that hascorrelation between successive data points, as is the case for ABR data,are not equal to the number of data points used in calculating thevariance estimate. For example, a time window containing 100 data pointsis normally assumed to have 99 degrees of freedom, but may actually onlyhave 10. This means that the distribution of the sample variance of sucha set of points will follow chi-square with 10 dof, not chi-square with99 dof. The distribution of the F_(SP) statistic will changeaccordingly. Experimental studies have shown that the effective dof in,say, a 10 ms window of ABR data vary slightly across subjects andmeasurement conditions. Since the dof in a individual subject are notknown exactly, but rather, only approximately, the Type I error rate(alpha, the significance level of the response detection test) will beonly approximately correct.

Thus a great strength of F_(SP) is that the F-distribution is valid. Thequalification is that the decision error rates are not known exactly,only approximately.

SUMMARY OF THE INVENTION

The subject invention provides a method for determining the statisticalprobability that an ABR to an acoustic stimulus is present in a testsubject. This allows the technician to make detection decisions that arevalid, consistent and efficient, and which have known and specifiablerates of error. These features offer substantial advantages over currentmethods, and are especially important in the design of effective andcost-efficient large-scale public health screening programs for infants.

The invention employs a computational algorithm that provides acontinuously evolving estimate of the probability of ABR presence asacquired data accumulates. It may be used to determine responseprobability and to govern when to stop the data acquisition. Thealgorithm offers substantial improvements over the best availablesemi-analytic method, namely F_(SP). In particular, the algorithmemployed by the present invention improves the power and efficiency ofresponse detection and improves the degree to which the Type I errorrate (alpha) and the power can be specified and controlled.

The algorithm utilizes the Hotelling T² test. New features are:

i. Application of a T²-based test to time-domain evoked potential data.

ii. Application of time-domain T² in a cumulative, sequential manner.

iii. The incorporation of a quantitative strategy to define the targetepoch, based on the relationship between non-centrality, degrees offreedom and epoch length.

iv. The test is completely analytic, with known degrees of freedom, andavoids any appeal to empirical normative data. It provides exact andvalid error rates and power.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates prior art calculation of a F_(SP) statistic for ABRwaveforms.

FIGS. 2a, 2 b is a functional flow diagram of the diagnostic procedureof the invention.

FIG. 3 diagrammatically illustrates the electrophysiologic datarecording procedure of the present invention.

FIG. 4 is a data plot comparing performance of the present inventionwith prior art F_(SP) and another type new of analysis.

FIG. 5 is a data plot comparing efficiency of the present invention withprior art F_(SP) and another new type of analysis.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, for purposes of explanation and notlimitation, specific details are set forth in order to provide athorough understanding of the present invention. However, it will beapparent to one skilled in the art that the present invention may bepracticed in other embodiments that depart from these specific details.In other instances, detailed descriptions of well-known methods anddevices are omitted so as to not obscure the description of the presentinvention with unnecessary detail. The invention set forth herein mayinclude elements of the well-known methods and devices disclosedhereinabove.

Power-Optimized Time-Domain Cumulative Sequential Hotelling T² Test.

It was noted earlier that one limitation of the standard F_(SP) test isits semi-analytic nature, which stems from the uncertainly about thetrue degrees of freedom of the test statistic in any specific test case.The present invention employs a Hotelling T² statistic. This is not avariance ratio test like F_(SP). Rather it is a test of thejoint-zeroness of the ABR data average, again at various selected timepoints post-stimulus. The Hotelling transform is described generally in“Analysis of a Complex of Statistical Variables into PrincipalComponents”, Hotelling, H. (1933), Journal of Educational Psychology,Vol. 24, pp 417-441.

For any target signal, it is clear that non-zeroness of the signal willtend to cause non-zero variance over time. However, it is obvious that,for example, if the signal is constant over the domain of interest, atest of zeroness may be positive but a test of variance will not be.(The variance of a constant being zero.) Thus, the statistics employedby the two embodiments of this invention address related but differentfacets of signal morphology, the details of which have not heretoforebeen explored systematically.

In the case of a time-variant waveform such as the ABR, the T² and theF_(SP) tests access both conjoint and disjoint waveform features. Theunique advantage of the T² test is that it is completely analytic. It isalso the most powerful of all tests of joint non-zeroness of multipledata items. It is highly robust, maintaining correctness of significancelevels even in the face of large departures from normality (Gaussianity)of source data. In practice, ABR data are approximately normallydistributed or can easily be transformed to be so.

The analytic nature of T² stems from its intrinsic ability to takeaccount of any pattern of correlation (covariance) among the datapoints. The sample variance-covariance matrix for the k-point datavector is incorporated into the statistic. This avoids any need toestimate or assume the degrees of freedom.

The T² statistic is well known in domain of multivariate statisticalanalysis. However, it has not heretofore been applied to time-domain ABRdata. The standard view of ABR data is as a time-series; this is totallydifferent from the multivariate viewpoint, which treats the data vectoras a single realization or data point, in a vector space of kdimensions. There has been a report of a very restricted T² applicationto the two components of a single harmonic in the Fourier spectrum ofthe ABR. This would, however, function very poorly as a responsedetection test, because the information contained in the time-domain ABRis distributed among many Fourier components; to focus on a singlecomponent would sustain massive power loss, rendering the techniquecompletely unsuitable for response detection in a mass screeningcontext. In fact, the single-harmonic application of the T² test is bothtrivial and a misnomer: the two components (real and imaginary, sine andcosine) of any Fourier harmonic are in fact statistically independent,vitiating the entire point of the T² approach, which is to accommodatecovariance among the data items. For the 2-component independent-measureproblem, the T² statistic degenerates to a simple chi-square statistic.

There are two other important features of the T² statistic as employedby the present invention. The first is that the power of the T² test isdiluted, as is the power of standard F_(SP), if the selected set of dataitems is not carefully focused on the target waveform. In the presentinvention, this focusing and power enhancement is achieved by defining aputative response region in terms of the mean square response energy perunit time. Regions of low energy are not included.

Secondly, the application of the T² test to ABR detection in the timedomain is done in a manner that is completely different to othermultivariate applications. Normally a sample of (multivariate) dataitems is collected and the T² test applied to the entire sample. In thisapplication, there is an acute need to stop data acquisition as soon aspossible, consistent with achieving satisfactory decision error rates.The amount of data required varies dramatically over subjects, dependingprimarily on the amplitude of the electrophysiologic “noise” thatobscures the genuine ABR. The approach to this problem is to apply theT² test in a sequential manner, to the accumulating dataset. Dataacquisition is terminated when the response is detected, when theresidual noise in the average record reaches a predetermined low value,or failing that, when a preset maximum number of sweeps is acquired.This iterative, cumulative sequential method of applying the T² test isunique.

The T² statistic used in the technique of this invention has the form:${\frac{N - k}{k\left( {N - 1} \right)} \cdot {N\left( {\overset{\_}{\underset{\sim}{x}} - \underset{\sim}{\mu_{0}}} \right)}^{\prime}}{{\underset{\sim}{S}}^{- 1}\left( {\overset{\_}{\underset{\sim}{x}} - \underset{\sim}{\mu_{0}}} \right)}$

where

N is the number of sweeps

k is the number of selected test points

{overscore (x)} is the average data vector for the k points

μ_(o) is the population mean vector under the null hypothesis, which forABR testing is the null vector, and

S⁻¹ is the inverse of the k×k covariance matrix

This statistic has the (central) F distribution with k and N-k dof, whenthe ABR is absent. When response is present, the distribution isnon-central F, with k and N-k dof and non-centrality parameter:$\underset{\sim}{\tau^{\prime}}{{\sum\limits_{\sim}}^{- 1}\underset{\sim}{\tau}}$

where τ is the true response ABR vector at the k selected time pointsand Σ⁻¹ is the inverse of the population variance-covariance matrix (ofwhich S is an estimate).

FIGS. 2a, 2 b is a flowchart of the screening test in accordance withthe second embodiment of this invention. The procedure begins withroutine setting of acquisition parameters for the A/D converterincluding sampling rate appropriate for ABR and data filtering etc. Inthe example used, 20 ms of activity is digitized at 10,000 Hz (200points). At this time the technician also decides the target P-value forthe test. For example, a P-value of 0.01 indicates 99% confidence thatthe ABR detected is an actual response. The maximum number of sweeps isselected, which indicates how long the testing should continue beforestopping. The test buffer length is also referred to as the block size.This is the sub-sample of sweeps between each recalculation of the teststatistic. Artifact rejection level is a voltage level. Because unwantedactivity such as muscle responses, have very large voltage relative tothe neural response, any sweep containing very large excursions is notused to avoid excessive contamination of the data.

The acquisition of each sweep of EEG is accomplished using standardtechniques. A schematic of the instrumentation is shown in FIG. 3. EEGactivity is acquired by means of scalp-applied, metal disk electrodesconnected by lead wires to a differential preamplifier. The preamplifiersubtracts signals recorded from two scalp placements to eliminate likecomponents of the recordings assumed to include noise (activity otherthan neural evoked potentials). Placement of these electrodes should becarefully chosen to optimize the recording of target waveforms. Forexample, the positive lead at the vertex (Cz) and negative at thehairline on the back of the neck or mastoid will be optimal forrecording an infant ABR in response to low-level signals. A two-channelrecording may be employed. Signals are amplified and band-pass filteredwith filter specifications chosen specifically to enhance the targetactivity.

EEG activity is sectioned into epochs or sweeps of user-determinedduration, for example 10-30 ms. Activity is digitized with a samplingrate appropriate for the spectral content of the signal. A triggeringmechanism is used to synchronize the sampling of each sweep and thepresentation of appropriate auditory stimuli with a user-selectedinter-stimulus interval. The stimulus is generally a 100 μs square-wavepulse that produces a click when applied to the appropriate transducer.

With reference again to FIG. 2, as each block of sweeps is collected,the calculation of the test statistic takes place. The associatedP-value is then determined and compared to the target P-value initiallyset by the technician. If the target is not reached, the entire processrepeats. Recording is halted when response presence or absence isdetermined in accordance with the target P-value. The device may or maynot have a hard-copy printout of response, or may have a more simplifiedindicator of the response decision such as “pass” or “fail”.

Experimental results

Twelve, healthy newborns were evaluated at the Infant Auditory ResearchLaboratory of Los Angeles County +University of Southern CaliforniaMedical Center, Women's and Children's Hospital. One or both ears wereassessed by standard ABR techniques using both 30 dB nHL click stimulior in no-stimulus conditions. In each condition, 10,000 individualsweeps of 20 ms duration were stored off-line for lab analysis. Data wasacquired via a Neuroscan “Synamps” amplifier and Scan acquisitionsoftware, data was digitized at 10k Hz and filtered from 100 to 3000 Hz.

Electrophysiologic recordings with click stimuli were analyzed with thepresent invention, with another algorithm d (a Point-Optimized VarianceRatio (POVR), which is a modified F_(SP) technique) and with standardF_(SP). Probability curves were constructed for each set of data usingstandard (120 pt) F_(SP), POVR with 4 and 10-point selections andHotelling T². The intersection of −log₁₀ p=2 revealed the number ofsweeps necessary in each condition to reach alpha of 0.01. Those dataare plotted in FIG. 4. A significant reduction in the number of sweepswas found for both the POVR and Hotelling algorithms when compared tostandard F_(SP). Efficiency ratios (#sweeps in standard F_(SP)condition/# sweeps in test condition) for 3 tests are shown in ascatterplot on FIG. 5.

The 120-point (standard) F_(SP) can be considered as a baseline againstwhich to evaluate the invention. It should be noted that each set ofmeasurements in a given baby constitutes an element of a random sampleof possible observed values of the statistics. Thus, fluctuation in thenumbers of sweeps required, and differences from case to case in therelationships between the statistics, are to be expected. Relative tothe 120-point F_(SP) baseline, the invention improves the efficiency ofmeasurement in all cases except case B11clickr. In several cases, theimprovement is dramatic (such as for B9clickl). Such a result may wellmake the difference as to whether any valid screening result at allcould be obtained practically in such a case. In general, the gains areexpressed by the average values of the efficiency ratios, which are veryfavorable.

The present invention has been described in the context of a screeningprocess utilizing auditory brainstem response (ABR). Another physiologicmeasure currently in use for evaluation of hearing status in newborninfants is otoacoustic emissions (OAE). Screening techniques using thismeasure have been shown to be fast and reasonably accurate inidentifying hearing impairment in newborns. As with ABR, OAE is amenableto objective response detection and automation. The detection algorithmsdescribed herein could also be applied to OAE with only minormodifications.

It will be recognized that the above described invention may be embodiedin other specific forms without departing from the spirit or essentialcharacteristics of the disclosure. Thus, it is understood that theinvention is not to be limited by the foregoing illustrative details,but rather is to be defined by the appended claims.

What is claimed is:
 1. A method for gathering and analyzing ABR signaldata generated in response to auditory stimuli to determine hearingcapacity of an individual comprising the steps of: (a) generating aplurality of auditory stimuli; (b) presenting said auditory stimuli to atest subject's ear; (c) collecting electrophysiologic signal data fromthe test subject within a time window following said each of a specifiednumber of auditory stimuli; (d) computing a cumulative test subjectaverage waveform from said collected electrophysiologic signal data; (e)computing a Hotelling T² statistic for said cumulative test subjectaverage waveform; (f) computing a probability value associated with saidcomputed Hotelling T² statistic; (g) repeating steps (a) through (f)adding to the cumulative test subject average waveform if said computedprobability value exceeds a predetermined threshold and declaring thatthe test subject responded to the auditory stimulus if the computedprobability value is below said predetermined threshold; and (h)terminating steps (a) through (f) and declaring that no response ispresent if a predetermined number of stimuli have been presented withoutthe computed probability value falling below the predeterminedthreshold.
 2. The method of claim 1 wherein said Hotelling T² statistichas the form:${\frac{N - k}{k\left( {N - 1} \right)} \cdot {N\left( {\overset{\_}{\underset{\sim}{x}} - \underset{\sim}{\mu_{0}}} \right)}^{\prime}}{{\underset{\sim}{S}}^{- 1}\left( {\overset{\_}{\underset{\sim}{x}} - \underset{\sim}{\mu_{0}}} \right)}$

where N is a number of times step (c) is repeated; k is a number ofselected test points; {overscore (x)} is an average data vector for saidk test points; μ_(o) is a population mean vector; and S⁻¹ is the inverseof a k×k covariance matrix.
 3. A system for gathering and analyzing ABRsignal data generated in response to auditory stimuli to determinehearing capacity of an individual comprising: (a) means for generating aplurality of auditory stimuli; (b) means for presenting said auditorystimuli to a test subject's ear; (c) means for collectingelectrophysiologic signal data from the test subject within a timewindow following each of a specified number of said auditory stimuli;(d) means for computing a cumulative test subject ABR waveform fromcollected electrophysiologic signal data; (e) means for computing aHotelling T² statistic for accumulative test subject average waveform;(f) means for computing probability value associated with a computedHotelling T² statistic; (g) means for iteratively operating (a) through(f) adding to the cumulative test subject average waveform if saidcomputed probability value exceeds a predetermined threshold anddeclaring that the test subject responded to the auditory stimulus ifthe computed probability value is below said predetermined threshold;and (h) means for terminating operation of (a) through (f) and declaringthat no response is present if a predetermined number of stimuli havebeen presented without the computed probability value falling below thepredetermined threshold.
 4. The system of claim 3 wherein said HotellingT² statistic has the form:${\frac{N - k}{k\left( {N - 1} \right)} \cdot {N\left( {\overset{\_}{\underset{\sim}{x}} - \underset{\sim}{\mu_{0}}} \right)}^{\prime}}{{\underset{\sim}{S}}^{- 1}\left( {\overset{\_}{\underset{\sim}{x}} - \underset{\sim}{\mu_{0}}} \right)}$

where N is a number of times electrophysiologic signal data iscollected; k is a number of selected test points; {overscore (x)} is anaverage data vector for said k test points; μ_(o) is a population meanvector; and S⁻¹ is the inverse of a k×k covariance matrix.